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Comparative Analysis of Triterpene Composition between Ganoderma lingzhi from China and G. lucidum from Slovakia under Different Growing Conditions

2020· article· en· W3047011041 on OpenAlexaff
Martin Pavlík, Shuai Zhou, Jingsong Zhang, Qingjiu Tang, Na Feng, Daniel Kurjak, Andrej Kunca

Bibliographic record

VenueInternational journal of medicinal mushrooms · 2020
Typearticle
Languageen
FieldMedicine
TopicFungal Biology and Applications
Canadian institutionsMinistry of Agriculture
Fundersnot available
KeywordsTerpeneBeechMedicinal fungiGanoderma lucidumMushroomMyceliumTriterpeneBotanyGanodermaBiologyFood scienceTraditional medicinePolysaccharideBiochemistry

Abstract

fetched live from OpenAlex

The mushroom today known as Ganoderma lingzhi has been used for centuries in the countries of Eastern Asia as a very important medicinal mushroom. It prefers growing on rotten wood of broadleaf trees and is mainly distributed in the tropics and subtropics. Its relative G. lucidum occurs naturally almost all the Earth, and it colonizes mostly oak and beech trees in Central Europe. G. lingzhi and G. lucidum are similar species. To obtain the qualitative parameters of G. lingzhi and G. lucidum, several strains (five G. lingzhi strains and five G. lucidum ones) were chosen and cultivated in both Slovakia and China, using wood chip (beech and oak) substrate and liquid fermentation method, respectively. It was found that there were more low-polarity triterpenes in G. lucidum, while G. lingzhi contained more high-polarity triterpenes. Beech substrate was more suitable for the accumulation of triterpenes in solid cultivation for both strains of G. lucidum and G. lingzhi. Strain C4 of G. lingzhi and strain K2 of G. lucidum contained higher triterpenes in either mycelium or fruiting bodies. Data in this study can help to identify these two species and bring a great benefit to the production of bioactive compounds of G. lucidum from Slovakia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.341
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2020
Admission routes1
Has abstractyes

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